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Fire Technology

, Volume 46, Issue 3, pp 719–741 | Cite as

Sensor Assisted Fire Fighting

  • Adam Cowlard
  • Wolfram Jahn
  • Cecilia Abecassis-Empis
  • Guillermo Rein
  • José L. ToreroEmail author
Article

Abstract

Fire detection and monitoring sensors, fire modelling, fire fighting and command and control are usually perceived as independent issues within fire safety. Sensor data is associated to detection and alarm and to some minor extent as a source of very basic information for building management or emergency response. The streams of data emerging from sensors are deemed to lead to a rapid information overload, so the pervasive sensor deployment (now common in modern buildings) is entirely independent of procedures associated to emergency management. Fire modelling follows a similar path because model output is not robust enough, not fast enough and the information generated by such simulations rapidly escalates in quantity and complexity so that no commander can assimilate it. Fire fighting is therefore left as an isolated activity that does not benefit much from sensor data or the potential of modelling the event. This separation is naturally induced by the complexity of a fire event and represents the biggest barrier to the useful development of sensor technology and fire modelling into emergency response. Therefore, current technology applied to fire is decades behind sensor development for other related areas like military operations or intruder security. There is no apparent use for more complex and expensive sensors. This paper describes the different processes that need to be studied to establish a path by which a collection of sensor data can be used to provide early detection, robust building management and adequate information to assist fire fighting operations.

Keywords

sensors modelling fire fighting forecast emergency response 

References

  1. 1.
    Snell J (2001) Towards a global agenda for fire research. In: Cox G (ed) Proceedings of the united engineering foundation conference, NY, pp 11–19 Google Scholar
  2. 2.
    Audouin L, Kolb G, Torero J, Most J (1995) Average centerline temperatures of a buoyant pool fire obtained by image processing of video recordings. Fire Saf J 24(2):167–187CrossRefGoogle Scholar
  3. 3.
    Davis W, Vettori R, Reneke P, Brassel L (2005) Workshop on the evaluation of a tactical decision aid display. NIST report NISTIR 7268Google Scholar
  4. 4.
    Cox G, Kumar S (2002) Modelling enclosure fires using CFD. In: DiNenno P (ed) SFPE handbook of fire protection engineering. Quincy, MA 02269, pp 3-194–3-218Google Scholar
  5. 5.
    Sohn H (2003) A review of structural health monitoring literature 1996–2001. In: Proceedings of the third world conference on structural control, pp 9–15Google Scholar
  6. 6.
    Abecassis-Empis C, Reszka P, Steinhaus T, Cowlard A, Biteau H, Welch S, Rein G, Torero JL (2008) Characterisation of Dalmarnock Fire Test One. Exp Therm Fluid Sci 32(7):1334–1343Google Scholar
  7. 7.
    Rein G, Abecassis-Empis C, Carvel R (eds) (2007) The Dalmarnock fire tests: experiments and modelling, 1st edn. ISBN 978-0-9557497-0-4. The University of Edinburgh. http://www.era.lib.ed.ac.uk/handle/1842/2037
  8. 8.
    McGrattan K (2003) Fire dynamics simulator (Version 4)—user’s manualGoogle Scholar
  9. 9.
    Rein G, Torero JL, Jahn W, Stern-Gottfried J, Ryder NL, Desanghere S, Lázaro M, Mowrer F, Coles A, Joyeux D, Alvear D, Capote JA, Jowsey A, Abecassis-Empis C, Reszka P (2009) Round-Robin study of a priori modelling predictions of the Dalmarnock Fire Test One. Fire Saf J (in press)Google Scholar
  10. 10.
    Bodor R, Jackson B, Papanikolopoulos N (2003) Vision-based human tracking and activity recognition. In: Proceedings of the 11th Mediterranean conference on control and automation in Rhodos, Greece, pp 18–20Google Scholar
  11. 11.
    Braidwood J (1830) On the construction of fire-engines and apparatus—the training of firemen, and the method of proceeding in cases of fire, 1st edn. Oliver & BoydGoogle Scholar
  12. 12.
    Jahn W, Rein G, Torero J (2008) The effect of model parameters on the simulation of fire dynamics. Fire Saf Sci (in press)Google Scholar
  13. 13.
    Upadhyay R, Beckett G, Pringle G, Potter S, Han S, Welch S, Usmani A, Torero J (2008) A system architecture for technology integrations in FireGrid: an integrated fire emergency response system for the built environment. Fire Saf Sci (in press)Google Scholar
  14. 14.
    Kalnay E (2003) Atmospheric modeling, data assimilation and predictability, 1st edn. CambridgeGoogle Scholar
  15. 15.
    Fernandez-Pello C (1995) The solid phase. In: Cox G (ed) Combustion fundamentals of fire. Academic Press LtdGoogle Scholar
  16. 16.
    Cowlard A, Auersperg L, Richon J, Rein G, Welch S, Usmani A, Torero J (2007) A simple methodology for sensor driven prediction of upward flame spread. Turkish J Eng Environ Sci 31(6):403–413Google Scholar
  17. 17.
    Tate A (2006) the helpful environment: geographically dispersed intelligent agents that collaborate. IEEE Intell Syst 21(3):57–61CrossRefGoogle Scholar
  18. 18.
    Tate A (2003) < I-N-C-A >: an ontology for mixed-initiative synthesis tasks. In: Proc workshop on mixed-initiative intelligent systems (MIIS) at the international joint conference on artificial intelligence (IJCAI-03), Acapulco, MexicoGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Adam Cowlard
    • 1
  • Wolfram Jahn
    • 1
  • Cecilia Abecassis-Empis
    • 1
  • Guillermo Rein
    • 1
  • José L. Torero
    • 1
    Email author
  1. 1.BRE Centre for Fire Safety EngineeringThe University of EdinburghEdinburghUK

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